• Imaging Science in Dentistry
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• v.52 no.1
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• pp.19-25
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• 2022

Purpose: This study evaluated the distance from the posterior root apices to the maxillary sinus floor (MSF) and the frequency of roots touching or protruding through the MSF using cone-beam computed tomography (CBCT). Materials and Methods: This study included 100 subjects divided into different vertical and anteroposterior skeletal growth patterns. On CBCT images, the distance from the posterior root apices to MSF was measured and the frequency of roots touching or protruding through the MSF was evaluated using NNT software (version 5.3.0.0; ImageWorks, Elmsford, NY, USA). Results: No statistically significant differences were found in the distance from the posterior root apices to the MSF among vertical skeletal groups (P>0.05). The palatal roots of the first molar and the palatal, mesio-buccal and disto-buccal roots of the second molars had significantly less distance from MSF in skeletal class II than in class III (P<0.05). The high-angle group had the highest frequencies of roots touching or protruding into the maxillary sinus (49.8%); the lowest proportion of these roots was found in skeletal class III (28.3%) and the highest proportion in class II (50.3%). Males had shorter distances from the posterior root apices to the MSF and higher frequencies of roots protruding through or touching the MSF than females. Conclusion: Anteroposterior skeletal growth patterns and sex affected the distances from the maxillary posterior roots to the MSF. The frequency of roots protruding into or touching the sinus was affected by both vertical and anteroposterior skeletal groups and sex. These findings have implications for dental practice.

• Journal of the Korean Society of Earth Science Education
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• v.8 no.2
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• pp.193-205
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• 2015

In this study, a Virtual geological Field Trip(VFT) learning program using 3D panorama virtual reality techniques was developed to learn about the Gongju city 7 area located in Chungcheongnam-do, Korea. The developed $360^{\circ}$ 3D VFT program can show every face of observational points and interact as zoom-in, zoom-out and image rotation. For the educational effects of the materials, it is provided with a compass, a protractor, enlarged images, pop-up windows, etc.. The program was applied to the class of 35 gifted students in middle school to investigate the effectiveness of the program. The results showed that positive responses of the students were 90% or more. When geological field trip problems like cost, safety, distance occur in geological learning procedure of middle school science, this VFT program can become as a supplementary learning material and a solution.

• Journal of Species Research
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• v.5 no.1
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• pp.179-187
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• 2016

As a subset investigation to discover indigenous prokaryotic species in Korea, a total of 21 bacterial strains assigned to the class Betaproteobacteria were isolated from a wide range of environmental samples which collected from fresh water, roots of plants, mineral water and soil from ginseng farm. Phylogenetic analysis based on 16S rRNA gene sequences indicated that 21 isolated strains were most closely related to the class Betaproteobacteria, with high 16S rRNA gene sequence similarity (>99.1%) and constructed a robust phylogenetic clade with the closest species in the class Betaproteobacteria. These isolated species have no previous report or publication in Korea; therefore 17 species in 14 genera of 6 families in the order Burkholderiales, 1 species in the order Methylophilales, 2 species in 2 genera of 1 family in the order Neisseriales are reported for betaproteobacterial species found in Korea. Gram reaction, colony and cell morphology, basic biochemical characteristics, isolation source, and strain IDs are also described in the species description section and as an image.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Archives of design research
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• v.14 no.1
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• pp.27-34
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• 2001

In order to design the products that impression or emotional taste influence the purchase, feedback is necessary as useful data for better idea sketches through users emotional evaluation in early stage of design process. On the other hand, it was required to make judgments individually in previous image evaluations for emotional evaluations such as semantic differential method (SD method) that objects have been considered as classified tendency. However those SD methods are not enough to reflect flexible human capability with similarity judgment in object perceptual process. Therefore, this study proposes a classification of stimulus based on intuitive judgment and a hierarchical image evaluation method based on analysis of hierarchical process and fuzzy integration. The evaluation will be conducted through the order of process, intuitive classification of objective stimulus and items, definition of representatives in each class. Evaluation for each image of the stimulus, calculation of prior raking based on fuzzy integration. The evaluation supportive software is developed to conduct this evaluation process under interactive environments.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.3
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• pp.347-360
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• 2009

The purpose of this study was to investigate the characteristics of a teacher's questions in each context of musical activity by analyzing those questions in kindergarten music activity qualitatively. The participant in research was a teacher in charge of the class of children aged 7. Analysis was carried out on the data collected for 3 months through participation observance note, recording material, interview material and weekly plan. As a result, the teacher's questions were leaned upon the convergent questions to confirm the reflection of information and the understanding of function in singing and performing activity. On the other hand, the teacher's questions were leaned upon the diffusive questions for the thought of integration of information in composition activity and upon the diffusive questions for representing feeling or image with no relation to musical meaning in appreciation activity. However, the teacher's questions for control were conducted together with one of the questions, the question mentioned ahead. The tacit control question in singing and performing activity and the explicit control question in composition activity were conducted together, and in appreciation activity, the simple questions to lead the class, though not with the purpose to control attention, was conducted together.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1309-1314
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• 2012

The SAMED filter is introduced as a wide class of multi-stage filters which encompass linear FIR and nonlinear order statistic filters. The output of SAMED filter is linear combination of sub-median outputs. In this paper, optimal SAMED filter is designed for images corrupted by various noise, and performance is analogized. The experimental result shows that the efficiency of each order of SAMED filters is depends on type of noise. It is shown that low order filters are effective in Gaussian environments but high order filters are effective in impulsive case. This result may be used to follow-up research on successive SAMED filters.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.